AI summary

This paper introduces a hybrid language model for German LVCSR using mixed sub-lexical units, including morphemes, syllables, and graphones, achieving a 5.0% relative reduction in Word Error Rate and recognizing 40% of Out-of-Vocabulary words, demonstrating improved lexical coverage and recognition performance.

Abstract

German is a highly inflected language with a large number of words derived from the same root. It makes use of a high degree of word compounding leading to high Out-of-vocabulary (OOV) rates, and Language Model (LM) perplexities. For such languages the use of sub-lexical units for Large Vocabulary Continuous Speech Recognition (LVCSR) becomes a natural choice. In this paper, we investigate the use of mixed types of sub-lexical units in the same recognition lexicon. Namely, morphemic or syllabic units combined with pronunciations called graphones, normal graphemic morphemes or syllables along with full-words. This mixture of units is used for building hybrid LMs suitable for open vocabulary LVCSR where the system operates over an open, constantly changing vocabulary like in broadcast news, political debates, etc. A relative reduction of around 5.0% in Word Error Rate (WER) is obtained compared to a traditional full-words system. Moreover, around 40% of the OOVs are recognized.